Digital soil mapping and crop modeling to define the spatially-explicit influence of soils on water-limited sugarcane yield

• Published in: Plant and soil Vol. 503; no. 1-2; pp. 349 - 369
• Main Authors: dos Santos, Natasha Valadares, Rizzo, Rodnei, Dias, Henrique Boriolo, Safanelli, José Lucas, Bonfatti, Benito Roberto, Sentelhas, Paulo Cesar, Amorim, Merilyn Taynara Accorsi, Mello, Danilo Cesar, Falcioni, Renan, Francelino, Marcio, Veloso, Gustavo Vieira, Demattê, José A. M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2024; Springer Nature B.V
• Subjects: Agricultural production; Agriculture; Algorithms; Biomedical and Life Sciences; Brazil; Computer simulation; crop models; Crop production; Crop yield; Crops; data collection; Digital mapping; Ecology; Food processing industry; hydrology; Information processing; Learning algorithms; Life Sciences; Machine learning; Mapping; Modelling; Monte Carlo method; Monte Carlo simulation; Performance evaluation; Plant Physiology; Plant Sciences; prediction; Predictions; Research Article; Simulation models; Soil analysis; soil heterogeneity; Soil improvement; soil map; Soil mapping; Soil maps; Soil properties; Soil Science & Conservation; Soil water; Sugarcane; texture; Uncertainty; yield mapping; Brazil; DSSAT/CANEGRO; Monte Carlo simulations; Spatial soil variability; Grid yield estimation

Background and Aims To enhance Brazilian sugarcane production sustainably, crop simulation models have been utilized. However, due to the lack of reliable information, particularly concerning soil variability, these models have shown limited performance for specific analyses. This study aims to eval...